An Artificial Neural Network and Genetic Algorithm Optimized Model for Biogas Production from Co-digestion of Seed Cake of Karanja and Cattle Dung
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- 作者：Debabrata Barik ; S. Murugan
- 关键词：Anaerobic digestion ; Biomass ; Biogas ; Genetic algorithm ; Artificial neural network
- 刊名：Waste and Biomass Valorization
- 出版年：2015
- 出版时间：December 2015
- 年：2015
- 卷：6
- 期：6
- 页码：1015-1027
- 全文大小：1,662 KB
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S. Murugan (1)
1. Department of Mechanical Engineering, National Institute of Technology, Rourkela, 769008, India - 刊物类别：Engineering
- 刊物主题：Engineering, general
Environment
Waste Management and Waste Technology
Industrial Pollution Prevention
Renewable and Green Energy
Environmental Engineering - 出版者：Springer Netherlands
- ISSN：1877-265X
文摘
In this study, experiments were conducted with four different proportions of seed cake of Karanja (SCK) and cattle dung (CD) mixture, for biogas production. 75, 50 and 25 % of the SCK on a mass basis were mixed with 25, 50 and 75 % of the CD and, named as S1, S2 and S3. For comparison, biogas obtained from 100 % CD (S4) was considered. The samples were kept in four different reactors, for 30 days of observation, and the yield of biogas from the samples S1, S2 and S3 was evaluated. Modeling was carried out for prediction and optimization of biogas production using ANN (artificial neural network) and the GA (genetic algorithm). A multi-layered feed-forward network with hidden neurons and linear output neurons was used for training the network using the input parameters pH, digestion time and C/N ratio for the yield of biogas. The performance of the neural network model was verified, and the correlation coefficients were found to be close to 1, for the samples. The experimental results on the biogas production were validated with the results of the neural network and optimized with the GA. The GA optimized values for pH, digestion time, and the C/N ratio of sample S3 were found to be 6.68, 14.22 days and 24.1:1, respectively. These optimized data can be used to monitor a large scale anaerobic plant. Among all samples, S3 gave a better result with respect to the pH, C/N (carbon/nitrogen) ratio, digestion time and biogas yield. Keywords Anaerobic digestion Biomass Biogas Genetic algorithm Artificial neural network